251
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Embedding multiple trajectories in simulated recurrent neural networks in a self-organizing manner. J Neurosci 2009; 29:13172-81. [PMID: 19846705 DOI: 10.1523/jneurosci.2358-09.2009] [Citation(s) in RCA: 75] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Complex neural dynamics produced by the recurrent architecture of neocortical circuits is critical to the cortex's computational power. However, the synaptic learning rules underlying the creation of stable propagation and reproducible neural trajectories within recurrent networks are not understood. Here, we examined synaptic learning rules with the goal of creating recurrent networks in which evoked activity would: (1) propagate throughout the entire network in response to a brief stimulus while avoiding runaway excitation; (2) exhibit spatially and temporally sparse dynamics; and (3) incorporate multiple neural trajectories, i.e., different input patterns should elicit distinct trajectories. We established that an unsupervised learning rule, termed presynaptic-dependent scaling (PSD), can achieve the proposed network dynamics. To quantify the structure of the trained networks, we developed a recurrence index, which revealed that presynaptic-dependent scaling generated a functionally feedforward network when training with a single stimulus. However, training the network with multiple input patterns established that: (1) multiple non-overlapping stable trajectories can be embedded in the network; and (2) the structure of the network became progressively more complex (recurrent) as the number of training patterns increased. In addition, we determined that PSD and spike-timing-dependent plasticity operating in parallel improved the ability of the network to incorporate multiple and less variable trajectories, but also shortened the duration of the neural trajectory. Together, these results establish one of the first learning rules that can embed multiple trajectories, each of which recruits all neurons, within recurrent neural networks in a self-organizing manner.
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252
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Network-state modulation of power-law frequency-scaling in visual cortical neurons. PLoS Comput Biol 2009; 5:e1000519. [PMID: 19779556 PMCID: PMC2740863 DOI: 10.1371/journal.pcbi.1000519] [Citation(s) in RCA: 59] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2009] [Accepted: 08/25/2009] [Indexed: 11/19/2022] Open
Abstract
Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of Vm activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the Vm reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the “effective” connectivity responsible for the dynamical signature of the population signals measured at different integration levels, from Vm to LFP, EEG and fMRI. Intracellular recording of neocortical neurons provides an opportunity of characterizing the statistical signature of the synaptic bombardment to which it is submitted. Indeed the membrane potential displays intense fluctuations which reflect the cumulative activity of thousands of input neurons. In sensory cortical areas, this measure could be used to estimate the correlational structure of the external drive. We show that changes in the statistical properties of network activity, namely the local correlation between neurons, can be detected by analyzing the power spectrum density (PSD) of the subthreshold membrane potential. These PSD can be fitted by a power-law function 1/fα in the upper temporal frequency range. In vivo recordings in primary visual cortex show that the α exponent varies with the statistics of the sensory input. Most remarkably, the exponent observed in the ongoing activity is indistinguishable from that evoked by natural visual statistics. These results are emulated by models which demonstrate that the exponent α is determined by the local level of correlation imposed in the recurrent network activity. Similar relationships are also reproduced in cortical neurons recorded in vitro with artificial synaptic inputs by controlling in computo the level of correlation in real time.
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253
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Levina A, Geisel T, Herrmann JM. Switching to criticality by synchronized input. BMC Neurosci 2009. [DOI: 10.1186/1471-2202-10-s1-p155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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254
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Schachter SC, Guttag J, Schiff SJ, Schomer DL. Advances in the application of technology to epilepsy: the CIMIT/NIO Epilepsy Innovation Summit. Epilepsy Behav 2009; 16:3-46. [PMID: 19780225 PMCID: PMC8118381 DOI: 10.1016/j.yebeh.2009.06.028] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
In 2008, a group of clinicians, scientists, engineers, and industry representatives met to discuss advances in the application of engineering technologies to the diagnosis and treatment of patients with epilepsy. The presentations also provided a guide for further technological development, specifically in the evaluation of patients for epilepsy surgery, seizure onset detection and seizure prediction, intracranial treatment systems, and extracranial treatment systems. This article summarizes the discussions and demonstrates that cross-disciplinary interactions can catalyze collaborations between physicians and engineers to address and solve many of the pressing unmet needs in epilepsy.
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Affiliation(s)
- Steven C Schachter
- Center for Integration of Medicine and Innovative Technology, Boston, MA, USA.
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255
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Hsu D, Hsu M. Zwanzig-Mori projection operators and EEG dynamics: deriving a simple equation of motion. PMC BIOPHYSICS 2009; 2:6. [PMID: 19594920 PMCID: PMC2728514 DOI: 10.1186/1757-5036-2-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2009] [Accepted: 07/13/2009] [Indexed: 11/24/2022]
Abstract
We present a macroscopic theory of electroencephalogram (EEG) dynamics based on the laws of motion that govern atomic and molecular motion. The theory is an application of Zwanzig-Mori projection operators. The result is a simple equation of motion that has the form of a generalized Langevin equation (GLE), which requires knowledge only of macroscopic properties. The macroscopic properties can be extracted from experimental data by one of two possible variational principles. These variational principles are our principal contribution to the formalism. Potential applications are discussed, including applications to the theory of critical phenomena in the brain, Granger causality and Kalman filters. PACS code: 87.19.lj
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Affiliation(s)
- David Hsu
- Department of Neurology, University of Wisconsin, Madison WI, USA.
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256
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Siekmeier PJ. Evidence of multistability in a realistic computer simulation of hippocampus subfield CA1. Behav Brain Res 2009; 200:220-31. [PMID: 19378385 DOI: 10.1016/j.bbr.2009.01.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The manner in which hippocampus processes neural signals is thought to be central to the memory encoding process. A theoretically oriented literature has suggested that this is carried out via "attractors" or distinctive spatio-temporal patterns of activity. However, these ideas have not been thoroughly investigated using computational models featuring both realistic single-cell physiology and detailed cell-to-cell connectivity. Here we present a 452 cell simulation based on Traub et al.'s pyramidal cell [Traub RD, Jefferys JG, Miles R, Whittington MA, Toth K. A branching dendritic model of a rodent CA3 pyramidal neurone. J Physiol (Lond) 1994;481:79-95] and interneuron [Traub RD, Miles R, Pyramidal cell-to-inhibitory cell spike transduction explicable by active dendritic conductances in inhibitory cell. J Comput Neurosci 1995;2:291-8] models, incorporating patterns of synaptic connectivity based on an extensive review of the neuroanatomic literature. When stimulated with a one second physiologically realistic input, our simulated tissue shows the ability to hold activity on-line for several seconds; furthermore, its spiking activity, as measured by frequency and interspike interval (ISI) distributions, resembles that of in vivo hippocampus. An interesting emergent property of the system is its tendency to transition from stable state to stable state, a behavior consistent with recent experimental findings [Sasaki T, Matsuki N, Ikegaya Y. Metastability of active CA3 networks. J Neurosci 2007;27:517-28]. Inspection of spike trains and simulated blockade of K(AHP) channels suggest that this is mediated by spike frequency adaptation. This finding, in conjunction with studies showing that apamin, a K(AHP) channel blocker, enhances the memory consolidation process in laboratory animals, suggests the formation of stable attractor states is central to the process by which memories are encoded. Ways that this methodology could shed light on the etiology of mental illness, such as schizophrenia, are discussed.
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Affiliation(s)
- Peter J Siekmeier
- Harvard Medical School and McLean Hospital, 115 Mill Street, Belmont, MA 02478, USA.
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257
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Principal component analysis of ensemble recordings reveals cell assemblies at high temporal resolution. J Comput Neurosci 2009; 29:309-325. [PMID: 19529888 PMCID: PMC2940043 DOI: 10.1007/s10827-009-0154-6] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2008] [Revised: 03/10/2009] [Accepted: 04/08/2009] [Indexed: 11/05/2022]
Abstract
Simultaneous recordings of many single neurons reveals unique insights into network processing spanning the timescale from single spikes to global oscillations. Neurons dynamically self-organize in subgroups of coactivated elements referred to as cell assemblies. Furthermore, these cell assemblies are reactivated, or replayed, preferentially during subsequent rest or sleep episodes, a proposed mechanism for memory trace consolidation. Here we employ Principal Component Analysis to isolate such patterns of neural activity. In addition, a measure is developed to quantify the similarity of instantaneous activity with a template pattern, and we derive theoretical distributions for the null hypothesis of no correlation between spike trains, allowing one to evaluate the statistical significance of instantaneous coactivations. Hence, when applied in an epoch different from the one where the patterns were identified, (e.g. subsequent sleep) this measure allows to identify times and intensities of reactivation. The distribution of this measure provides information on the dynamics of reactivation events: in sleep these occur as transients rather than as a continuous process.
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259
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Abstract
We propose that the critical function of sleep is to prevent uncontrolled neuronal feedback while allowing rapid responses and prolonged retention of short-term memories. Through learning, the brain is tuned to react optimally to environmental challenges. Optimal behavior often requires rapid responses and the prolonged retention of short-term memories. At a neuronal level, these correspond to recurrent activity in local networks. Unfortunately, when a network exhibits recurrent activity, small changes in the parameters or conditions can lead to runaway oscillations. Thus, the very changes that improve the processing performance of the network can put it at risk of runaway oscillation. To prevent this, stimulus-dependent network changes should be permitted only when there is a margin of safety around the current network parameters. We propose that the essential role of sleep is to establish this margin by exposing the network to a variety of inputs, monitoring for erratic behavior, and adjusting the parameters. When sleep is not possible, an emergency mechanism must come into play, preventing runaway behavior at the expense of processing efficiency. This is tiredness.
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260
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Peyrache A, Khamassi M, Benchenane K, Wiener SI, Battaglia FP. Replay of rule-learning related neural patterns in the prefrontal cortex during sleep. Nat Neurosci 2009; 12:919-26. [PMID: 19483687 DOI: 10.1038/nn.2337] [Citation(s) in RCA: 483] [Impact Index Per Article: 32.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2008] [Accepted: 04/17/2009] [Indexed: 11/09/2022]
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261
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Priesemann V, Munk MHJ, Wibral M. Subsampling effects in neuronal avalanche distributions recorded in vivo. BMC Neurosci 2009; 10:40. [PMID: 19400967 PMCID: PMC2697147 DOI: 10.1186/1471-2202-10-40] [Citation(s) in RCA: 82] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2008] [Accepted: 04/29/2009] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Many systems in nature are characterized by complex behaviour where large cascades of events, or avalanches, unpredictably alternate with periods of little activity. Snow avalanches are an example. Often the size distribution f(s) of a system's avalanches follows a power law, and the branching parameter sigma, the average number of events triggered by a single preceding event, is unity. A power law for f(s), and sigma = 1, are hallmark features of self-organized critical (SOC) systems, and both have been found for neuronal activity in vitro. Therefore, and since SOC systems and neuronal activity both show large variability, long-term stability and memory capabilities, SOC has been proposed to govern neuronal dynamics in vivo. Testing this hypothesis is difficult because neuronal activity is spatially or temporally subsampled, while theories of SOC systems assume full sampling. To close this gap, we investigated how subsampling affects f(s) and sigma by imposing subsampling on three different SOC models. We then compared f(s) and sigma of the subsampled models with those of multielectrode local field potential (LFP) activity recorded in three macaque monkeys performing a short term memory task. RESULTS Neither the LFP nor the subsampled SOC models showed a power law for f(s). Both, f(s) and sigma, depended sensitively on the subsampling geometry and the dynamics of the model. Only one of the SOC models, the Abelian Sandpile Model, exhibited f(s) and sigma similar to those calculated from LFP activity. CONCLUSION Since subsampling can prevent the observation of the characteristic power law and sigma in SOC systems, misclassifications of critical systems as sub- or supercritical are possible. Nevertheless, the system specific scaling of f(s) and sigma under subsampling conditions may prove useful to select physiologically motivated models of brain function. Models that better reproduce f(s) and sigma calculated from the physiological recordings may be selected over alternatives.
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Affiliation(s)
- Viola Priesemann
- Department of Neurophysiology, Max Planck Institute for Brain Research, Deutschordenstrasse 46, D-60528 Frankfurt am Main, Germany.
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262
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Early-stage waves in the retinal network emerge close to a critical state transition between local and global functional connectivity. J Neurosci 2009; 29:1077-86. [PMID: 19176816 DOI: 10.1523/jneurosci.4880-08.2009] [Citation(s) in RCA: 69] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
A novel, biophysically realistic model for early-stage, acetylcholine-mediated retinal waves is presented. In this model, neural excitability is regulated through a slow after-hyperpolarization (sAHP) operating on two different temporal scales. As a result, the simulated network exhibits competition between a desynchronizing effect of spontaneous, cell-intrinsic bursts, and the synchronizing effect of synaptic transmission during retinal waves. Cell-intrinsic bursts decouple the retinal network through activation of the sAHP current, and we show that the network is capable of operating at a transition point between purely local and global functional connectedness, which corresponds to a percolation phase transition. Multielectrode array recordings show that, at this point, the properties of retinal waves are reliably predicted by the model. These results indicate that early spontaneous activity in the developing retina is regulated according to a very specific principle, which maximizes randomness and variability in the resulting activity patterns.
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263
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Broadband criticality of human brain network synchronization. PLoS Comput Biol 2009; 5:e1000314. [PMID: 19300473 PMCID: PMC2647739 DOI: 10.1371/journal.pcbi.1000314] [Citation(s) in RCA: 301] [Impact Index Per Article: 20.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2008] [Accepted: 02/02/2009] [Indexed: 11/19/2022] Open
Abstract
Self-organized criticality is an attractive model for human brain dynamics, but there has been little direct evidence for its existence in large-scale systems measured by neuroimaging. In general, critical systems are associated with fractal or power law scaling, long-range correlations in space and time, and rapid reconfiguration in response to external inputs. Here, we consider two measures of phase synchronization: the phase-lock interval, or duration of coupling between a pair of (neurophysiological) processes, and the lability of global synchronization of a (brain functional) network. Using computational simulations of two mechanistically distinct systems displaying complex dynamics, the Ising model and the Kuramoto model, we show that both synchronization metrics have power law probability distributions specifically when these systems are in a critical state. We then demonstrate power law scaling of both pairwise and global synchronization metrics in functional MRI and magnetoencephalographic data recorded from normal volunteers under resting conditions. These results strongly suggest that human brain functional systems exist in an endogenous state of dynamical criticality, characterized by a greater than random probability of both prolonged periods of phase-locking and occurrence of large rapid changes in the state of global synchronization, analogous to the neuronal “avalanches” previously described in cellular systems. Moreover, evidence for critical dynamics was identified consistently in neurophysiological systems operating at frequency intervals ranging from 0.05–0.11 to 62.5–125 Hz, confirming that criticality is a property of human brain functional network organization at all frequency intervals in the brain's physiological bandwidth. Systems in a critical state are poised on the cusp of a transition between ordered and random behavior. At this point, they demonstrate complex patterning of fluctuations at all scales of space and time. Criticality is an attractive model for brain dynamics because it optimizes information transfer, storage capacity, and sensitivity to external stimuli in computational models. However, to date there has been little direct experimental evidence for critical dynamics of human brain networks. Here, we considered two measures of functional coupling or phase synchronization between components of a dynamic system: the phase lock interval or duration of synchronization between a specific pair of time series or processes in the system and the lability of global synchronization among all pairs of processes. We confirmed that both synchronization metrics demonstrated scale invariant behaviors in two computational models of critical dynamics as well as in human brain functional systems oscillating at low frequencies (<0.5 Hz, measured using functional MRI) and at higher frequencies (1–125 Hz, measured using magnetoencephalography). We conclude that human brain functional networks demonstrate critical dynamics in all frequency intervals, a phenomenon we have described as broadband criticality.
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264
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Pan L, Song X, Xiang G, Wong A, Xing W, Cheng J. First-spike rank order as a reliable indicator of burst initiation and its relation with early-to-fire neurons. IEEE Trans Biomed Eng 2009; 56:1673-82. [PMID: 19272980 DOI: 10.1109/tbme.2009.2015652] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In this paper, we study the spontaneous cortical neuronal network in hopes of finding a reliable indicator of burst initiation pathway, which would allow us to study burst initiation in conjunction with burst propagation in future research. Electrical activity is recorded using a 96-electrode microelectrode array on a weekly batch culture (half of the medium was replaced twice every week). We hypothesize that the first-spike onset sequence, which we call first-spike rank order (FSRO) is a reliable indicator of burst initiation, and verified our hypothesis by studying evoked bursts using rearranged rank probability matrices. Under similar conditions, stimulating the same site reliably reproduces the same FSRO. Spontaneous bursts can be classified based on their FSRO using dendrogram clustering. Bursts with different first-spike sequences showed evidence of sharing common early-to-fire neurons, but early-to-fire neurons only consist of a minority of neuronal activity during burst initiation, which is in partial accordance with existing literature. In the study of early-to-fire neurons, we also noticed that our batch-cultured network did not show clear preburst activity, which may indicate fundamental difference compared to continuous perfusion culture.
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Affiliation(s)
- Liangbin Pan
- Medical Systems Biology Research Center, School of Medicine, Tsinghua University, Beijing 100084, China.
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265
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Albus K, Sinske K, Heinemann U. Independent positioning of microelectrodes for multisite recordings in vitro. J Neurosci Methods 2009; 176:182-5. [PMID: 18822315 DOI: 10.1016/j.jneumeth.2008.09.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2008] [Revised: 08/25/2008] [Accepted: 09/01/2008] [Indexed: 10/21/2022]
Abstract
A robust and easy to handle, inexpensive multisite recording system is described which allows independent positioning of several microelectrodes with high precision axial movement of each electrode. The basic units of the system are a manually operated manipulator for left-right and up-down movement and a micrometer for positioning in the direction of the micrometer axis. The micrometer is actuated with a DC-motor operated by a stand-alone controller module allowing for remote control of the motor in either step mode or continuous mode. The multisite recording system has been proven to allow stable simultaneous recordings of single unit and population activities, extracellular ion concentrations and intracellular potentials in organotypic hippocampal slice cultures (OHSCs) of rat.
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Affiliation(s)
- Klaus Albus
- Institut für Neurophysiologie, Johannes Müller Centre of Physiology, CCM, Charité-Universitätsmedizin Berlin, Tucholskystr. 2, D-10117 Berlin, Germany.
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266
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Pajevic S, Plenz D. Efficient network reconstruction from dynamical cascades identifies small-world topology of neuronal avalanches. PLoS Comput Biol 2009; 5:e1000271. [PMID: 19180180 PMCID: PMC2615076 DOI: 10.1371/journal.pcbi.1000271] [Citation(s) in RCA: 90] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2008] [Accepted: 12/10/2008] [Indexed: 11/18/2022] Open
Abstract
Cascading activity is commonly found in complex systems with directed
interactions such as metabolic networks, neuronal networks, or disease spreading
in social networks. Substantial insight into a system's organization
can be obtained by reconstructing the underlying functional network architecture
from the observed activity cascades. Here we focus on Bayesian approaches and
reduce their computational demands by introducing the Iterative Bayesian (IB)
and Posterior Weighted Averaging (PWA) methods. We introduce a special case of
PWA, cast in nonparametric form, which we call the normalized count (NC)
algorithm. NC efficiently reconstructs random and small-world functional network
topologies and architectures from subcritical, critical, and supercritical
cascading dynamics and yields significant improvements over commonly used
correlation methods. With experimental data, NC identified a functional and
structural small-world topology and its corresponding traffic in cortical
networks with neuronal avalanche dynamics. In many complex systems found across disciplines, such as biological cells and
organisms, social networks, economic systems, and the Internet, individual
elements interact with each other, thereby forming large networks whose
structure is often not known. In these complex networks, local events can easily
propagate, resulting in diverse spatio-temporal activity cascades, or
avalanches. Examples of such cascading activity are the propagation of diseases
in social networks, cascades of chemical reactions inside a cell, the
propagation of neuronal activity in the brain, and e-mail forwarding on the
Internet. Although the observation of a single cascade provides limited insight
into the organization of a complex network, the observation of many cascades
allows for the reconstruction of very robust features of network organization,
providing valuable insight into network function as well as network failure. The
current work develops new algorithms for an efficient reconstruction of
relatively large networks in the context of cascading activity. When applied to
the brain, these algorithms uncover the structural and functional features of
gray matter networks that display activity cascades in the form of neuronal
avalanches.
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Affiliation(s)
- Sinisa Pajevic
- Mathematical and Statistical Computing Laboratory, Division of
Computational Bioscience, Center for Information Technology, National Institutes
of Health, Bethesda, Maryland, United States of America
| | - Dietmar Plenz
- Section on Critical Brain Dynamics, Laboratory of Systems Neuroscience,
National Institute of Mental Health, National Institutes of Health, Bethesda,
Maryland, United States of America
- * E-mail:
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267
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Alvarez-Lacalle E, Moses E. Slow and fast pulses in 1-D cultures of excitatory neurons. J Comput Neurosci 2009; 26:475-93. [DOI: 10.1007/s10827-008-0123-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2008] [Revised: 09/29/2008] [Accepted: 11/04/2008] [Indexed: 10/21/2022]
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268
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Baruchi I, Volman V, Raichman N, Shein M, Ben-Jacob E. The emergence and properties of mutual synchronization in in vitro coupled cortical networks. Eur J Neurosci 2009; 28:1825-35. [PMID: 18973597 DOI: 10.1111/j.1460-9568.2008.06487.x] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We have studied the emergence of mutual synchronization and activity propagation in coupled neural networks from rat cortical cells grown on a micro-electrode array for parallel activity recording of dozens of neurons. The activity of each sub-network by itself is marked by the formation of synchronized bursting events (SBE) - short time windows of rapid neuronal firing. The joint activity of two coupled networks is characterized by the formation of mutual synchronization, i.e. the formation of SBE whose activity starts at one sub-network and then propagates to the other. The sub-networks switch roles in initiating the mutual SBE. However, spontaneous propagation (initiation) asymmetry emerges - one of the sub-networks takes on the role of initiating substantially more mutual SBE than the other, despite the fact that the two are engineered to be similar in size and cell density. Analysis of the interneuron correlations in the SBE also reveals the emergence of activity (function) asymmetry - one sub-network develops a more organized structure of correlations. We also show activity propagation and mutual synchronization in four coupled networks. Using computer simulations, we propose that the function asymmetry reflects asymmetry between the internal connectivity of the two networks, whereas the propagation asymmetry reflects asymmetry in the connectivity between the sub-networks. These results agree with the experimental findings that the initiation and function asymmetry can be separately regulated, which implies that information transfer (activity propagation) and information processing (function) can be regulated separately in coupled neural networks.
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Affiliation(s)
- Itay Baruchi
- School of Physics & Astronomy, Raymond & Beverly Sackler Faculty of Exact Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel
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269
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Ikegaya Y, Matsumoto W, Chiou HY, Yuste R, Aaron G. Statistical significance of precisely repeated intracellular synaptic patterns. PLoS One 2008; 3:e3983. [PMID: 19096523 PMCID: PMC2599887 DOI: 10.1371/journal.pone.0003983] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2008] [Accepted: 11/18/2008] [Indexed: 11/19/2022] Open
Abstract
Can neuronal networks produce patterns of activity with millisecond accuracy? It may seem unlikely, considering the probabilistic nature of synaptic transmission. However, some theories of brain function predict that such precision is feasible and can emerge from the non-linearity of the action potential generation in circuits of connected neurons. Several studies have presented evidence for and against this hypothesis. Our earlier work supported the precision hypothesis, based on results demonstrating that precise patterns of synaptic inputs could be found in intracellular recordings from neurons in brain slices and in vivo. To test this hypothesis, we devised a method for finding precise repeats of activity and compared repeats found in the data to those found in surrogate datasets made by shuffling the original data. Because more repeats were found in the original data than in the surrogate data sets, we argued that repeats were not due to chance occurrence. Mokeichev et al. (2007) challenged these conclusions, arguing that the generation of surrogate data was insufficiently rigorous. We have now reanalyzed our previous data with the methods introduced from Mokeichev et al. (2007). Our reanalysis reveals that repeats are statistically significant, thus supporting our earlier conclusions, while also supporting many conclusions that Mokeichev et al. (2007) drew from their recent in vivo recordings. Moreover, we also show that the conditions under which the membrane potential is recorded contributes significantly to the ability to detect repeats and may explain conflicting results. In conclusion, our reevaluation resolves the methodological contradictions between Ikegaya et al. (2004) and Mokeichev et al. (2007), but demonstrates the validity of our previous conclusion that spontaneous network activity is non-randomly organized.
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Affiliation(s)
- Yuji Ikegaya
- Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan
- Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, Tokyo, Japan
| | - Wataru Matsumoto
- Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan
| | - Huei-Yu Chiou
- Laboratory of Chemical Pharmacology, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan
| | - Rafael Yuste
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia University, New York, New York, United States of America
| | - Gloster Aaron
- Biology Department, Neuroscience & Behavior Program, Hall-Atwater & Shanklin Labs, Wesleyan University, Middletown, Connecticut, United States of America
- * E-mail:
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270
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Werner G. Viewing brain processes as Critical State Transitions across levels of organization: Neural events in Cognition and Consciousness, and general principles. Biosystems 2008; 96:114-9. [PMID: 19124060 DOI: 10.1016/j.biosystems.2008.11.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2008] [Revised: 09/11/2008] [Accepted: 11/19/2008] [Indexed: 11/15/2022]
Abstract
In this theoretical and speculative essay, I propose that insights into certain aspects of neural system functions can be gained from viewing brain function in terms of the branch of Statistical Mechanics currently referred to as "Modern Critical Theory" [Stanley, H.E., 1987. Introduction to Phase Transitions and Critical Phenomena. Oxford University Press; Marro, J., Dickman, R., 1999. Nonequilibrium Phase Transitions in Lattice Models. Cambridge University Press, Cambridge, UK]. The application of this framework is here explored in two stages: in the first place, its principles are applied to state transitions in global brain dynamics, with benchmarks of Cognitive Neuroscience providing the relevant empirical reference points. The second stage generalizes to suggest in more detail how the same principles could also apply to the relation between other levels of the structural-functional hierarchy of the nervous system and between neural assemblies. In this view, state transitions resulting from the processing at one level are the input to the next, in the image of a 'bucket brigade', with the content of each bucket being passed on along the chain, after having undergone a state transition. The unique features of a process of this kind will be discussed and illustrated.
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Affiliation(s)
- Gerhard Werner
- Department of Biomedical Engineering, University of Texas, Austin, 78712-02308, USA.
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271
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Cadotte AJ, DeMarse TB, He P, Ding M. Causal measures of structure and plasticity in simulated and living neural networks. PLoS One 2008; 3:e3355. [PMID: 18839039 PMCID: PMC2556387 DOI: 10.1371/journal.pone.0003355] [Citation(s) in RCA: 69] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2008] [Accepted: 08/02/2008] [Indexed: 11/21/2022] Open
Abstract
A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a network's structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify “causal” relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal relationships among multiple measures is desired. These techniques can be especially useful when the interactions among those measures are highly complex, difficult to untangle, and maybe changing over time.
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Affiliation(s)
- Alex J. Cadotte
- Department of Biomedical Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Thomas B. DeMarse
- Department of Biomedical Engineering, University of Florida, Gainesville, Florida, United States of America
- * E-mail:
| | - Ping He
- Department of Biomedical Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Mingzhou Ding
- Department of Biomedical Engineering, University of Florida, Gainesville, Florida, United States of America
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272
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Hsu D, Chen W, Hsu M, Beggs JM. An open hypothesis: is epilepsy learned, and can it be unlearned? Epilepsy Behav 2008; 13:511-22. [PMID: 18573694 PMCID: PMC2611958 DOI: 10.1016/j.yebeh.2008.05.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2008] [Revised: 05/13/2008] [Accepted: 05/14/2008] [Indexed: 10/21/2022]
Abstract
Plasticity is central to the ability of a neural system to learn and also to its ability to develop spontaneous seizures. What is the connection between the two? Learning itself is known to be a destabilizing process at the algorithmic level. We have investigated necessary constraints on a spontaneously active Hebbian learning system and find that the ability to learn appears to confer an intrinsic vulnerability to epileptogenesis on that system. We hypothesize that epilepsy arises as an abnormal learned response of such a system to certain repeated provocations. This response is a network-level effect. If epilepsy really is a learned response, then it should be possible to reverse it, that is, to unlearn epilepsy. Unlearning epilepsy may then provide a new approach to its treatment.
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Affiliation(s)
- David Hsu
- Department of Neurology, University of Wisconsin, Madison, WI 53792, USA.
| | - Wei Chen
- Department of Physics, Indiana University, Bloomington IN
| | - Murielle Hsu
- Department of Neurology, University of Wisconsin, Madison WI
| | - John M. Beggs
- Department of Physics, Indiana University, Bloomington IN
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273
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Johnson HA, Buonomano DV. A method for chronic stimulation of cortical organotypic cultures using implanted electrodes. J Neurosci Methods 2008; 176:136-43. [PMID: 18835297 DOI: 10.1016/j.jneumeth.2008.08.037] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2008] [Revised: 08/29/2008] [Accepted: 08/29/2008] [Indexed: 10/21/2022]
Abstract
The neural mechanisms underlying some forms of learning and memory require hours or days to be expressed; however it has proven difficult to study these slowly developing forms of plasticity in reduced preparations due to the short-term nature of acute slice preparations and the fact that most culture preparations lack exposure to structured external input, which plays a critical role in normal cortical development and plasticity. To address this limitation, we developed a method for chronic stimulation of organotypic slice cultures using implanted microelectrodes. This method imparts the ability to apply patterned stimulation to cortical tissue for hours or days, and allows intracellular electrophysiological recordings before and after the stimulation. Importantly, the permanent implantation of the electrodes in the tissue assures that the same neuronal pathways are being excited both during the chronic stimulation while the cultures are in the incubator and while recording in the testing phase. This technique establishes a reduced model for studying experience-dependent plasticity.
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Affiliation(s)
- Hope A Johnson
- Departments of Neurobiology and Psychology, and Brain Research Institute, University of California, Los Angeles, Los Angeles, CA 90095, United States
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274
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Shein M, Volman V, Raichman N, Hanein Y, Ben-Jacob E. Management of synchronized network activity by highly active neurons. Phys Biol 2008; 5:036008. [DOI: 10.1088/1478-3975/5/3/036008] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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275
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Lytton WW, Orman R, Stewart M. Broadening of activity with flow across neural structures. Perception 2008; 37:401-7. [PMID: 18491717 DOI: 10.1068/p5871] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Synfire chains have long been suggested as a substrate for perception and information processing in the nervous system. However, embedding activation chains in a densely connected nervous matrix risks spread of signal that will obscure or obliterate the message. We used computer modeling and physiological measurements in rat hippocampus to assess this problem of activity broadening. We simulated a series of neural modules with feedforward propagation and random connectivity within each module and from one module to the next. We found that activity broadened as it propagated from one module to the next. This occurred over a wide array of parameters with greater broadening seen with increasing excitatory-excitatory synaptic strength. Activity broadening correlated positively with propagation velocity. Multi-electrode measurements of activity propagation in disinhibited CA1 slice demonstrated broadening of about 50% over 1 mm. Such broadening is a problem for information transfer that must be dealt with in a fully functioning nervous system.
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Affiliation(s)
- William W Lytton
- Downstate Medical Center, State University of New York, 450 Clarkson Avenue, Brooklyn, New York, NY 11203, USA.
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276
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Poil SS, van Ooyen A, Linkenkaer-Hansen K. Avalanche dynamics of human brain oscillations: relation to critical branching processes and temporal correlations. Hum Brain Mapp 2008; 29:770-7. [PMID: 18454457 DOI: 10.1002/hbm.20590] [Citation(s) in RCA: 73] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Human brain oscillations fluctuate erratically in amplitude during rest and exhibit power-law decay of temporal correlations. It has been suggested that this dynamics reflects self-organized activity near a critical state. In this framework, oscillation bursts may be interpreted as neuronal avalanches propagating in a network with a critical branching ratio. However, a direct comparison of the temporal structure of ongoing oscillations with that of activity propagation in a model network with critical connectivity has never been made. Here, we simulate branching processes and characterize the activity propagation in terms of avalanche life-time distributions and temporal correlations. An equivalent analysis is introduced for characterizing ongoing oscillations in the alpha-frequency band recorded with magnetoencephalography (MEG) during rest. We found that models with a branching ratio near the critical value of one exhibited power-law scaling in life-time distributions with similar scaling exponents as observed in the MEG data. The models reproduced qualitatively the power-law decay of temporal correlations in the human data; however, the correlations in the model appeared on time scales only up to the longest avalanche, whereas human data indicate persistence of correlations on time scales corresponding to several burst events. Our results support the idea that neuronal networks generating ongoing alpha oscillations during rest operate near a critical state, but also suggest that factors not included in the simple classical branching process are needed to account for the complex temporal structure of ongoing oscillations during rest on time scales longer than the duration of individual oscillation bursts.
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Affiliation(s)
- Simon-Shlomo Poil
- Department of Experimental Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands
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277
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Corner MA. Spontaneous neuronal burst discharges as dependent and independent variables in the maturation of cerebral cortex tissue cultured in vitro: a review of activity-dependent studies in live 'model' systems for the development of intrinsically generated bioelectric slow-wave sleep patterns. ACTA ACUST UNITED AC 2008; 59:221-44. [PMID: 18722470 DOI: 10.1016/j.brainresrev.2008.08.001] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2008] [Revised: 08/01/2008] [Accepted: 08/05/2008] [Indexed: 10/21/2022]
Abstract
A survey is presented of recent experiments which utilize spontaneous neuronal spike trains as dependent and/or independent variables in developing cerebral cortex cultures when synaptic transmission is interfered with for varying periods of time. Special attention is given to current difficulties in selecting suitable preparations for carrying out biologically relevant developmental studies, and in applying spike-train analysis methods with sufficient resolution to detect activity-dependent age and treatment effects. A hierarchy of synchronized nested burst discharges which approximate early slow-wave sleep patterns in the intact organism is established as a stable basis for isolated cortex function. The complexity of reported long- and short-term homeostatic responses to experimental interference with synaptic transmission is reviewed, and the crucial role played by intrinsically generated bioelectric activity in the maturation of cortical networks is emphasized.
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Affiliation(s)
- Michael A Corner
- Netherlands Institute for Brain Research, Amsterdam, The Netherlands.
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278
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Shen X, Lin X, Wilde PD. Oscillations and Spiking Pairs: Behavior of a Neuronal Model with STDP Learning. Neural Comput 2008; 20:2037-69. [DOI: 10.1162/neco.2008.08-06-317] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
In a biologically plausible but computationally simplified integrate-and-fire neuronal population, it is observed that transient synchronized spikes can occur repeatedly. However, groups with different properties exhibit different periods and different patterns of synchrony. We include learning mechanisms in these models. The effects of spike-timing-dependent plasticity have been known to play a distinct role in information processing in the central nervous system for several years. In this letter, neuronal models with dynamical synapses are constructed, and we analyze the effect of STDP on collective network behavior, such as oscillatory activity, weight distribution, and spike timing precision. We comment on how information is encoded by the neuronal signaling, when synchrony groups may appear, and what could contribute to the uncertainty in decision making.
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Affiliation(s)
- Xi Shen
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2BT, U.K
| | - Xiaobin Lin
- Department of Computer Science, Heriot-Watt University, Edinburgh EH14 4AS, U.K
| | - Philippe De Wilde
- Department of Computer Science, Heriot-Watt University, Edinburgh EH14 4AS, U.K
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279
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Teramae JN, Fukai T. Complex evolution of spike patterns during burst propagation through feed-forward networks. BIOLOGICAL CYBERNETICS 2008; 99:105-114. [PMID: 18685860 DOI: 10.1007/s00422-008-0246-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2007] [Accepted: 06/03/2008] [Indexed: 05/26/2023]
Abstract
Stable signal transmission is crucial for information processing by the brain. Synfire-chains, defined as feed-forward networks of spiking neurons, are a well-studied class of circuit structure that can propagate a packet of single spikes while maintaining a fixed packet profile. Here, we studied the stable propagation of spike bursts, rather than single spike activities, in a feed-forward network of a general class of excitable bursting neurons. In contrast to single spikes, bursts can propagate stably without converging to any fixed profiles. Spike timings of bursts continue to change cyclically or irregularly during propagation depending on intrinsic properties of the neurons and the coupling strength of the network. To find the conditions under which bursts lose fixed profiles, we propose an analysis based on timing shifts of burst spikes similar to the phase response analysis of limit-cycle oscillators.
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Affiliation(s)
- Jun-nosuke Teramae
- Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute, Hirosawa 2-1, Wako, Saitama, 351-0198, Japan.
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280
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Bettencourt LMA, Gintautas V, Ham MI. Identification of functional information subgraphs in complex networks. PHYSICAL REVIEW LETTERS 2008; 100:238701. [PMID: 18643550 DOI: 10.1103/physrevlett.100.238701] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2007] [Indexed: 05/26/2023]
Abstract
We present a general information theoretic approach for identifying functional subgraphs in complex networks. We show that the uncertainty in a variable can be written as a sum of information quantities, where each term is generated by successively conditioning mutual informations on new measured variables in a way analogous to a discrete differential calculus. The analogy to a Taylor series suggests efficient optimization algorithms for determining the state of a target variable in terms of functional groups of other nodes. We apply this methodology to electrophysiological recordings of cortical neuronal networks grown in vitro. Each cell's firing is generally explained by the activity of a few neurons. We identify these neuronal subgraphs in terms of their redundant or synergetic character and reconstruct neuronal circuits that account for the state of target cells.
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Affiliation(s)
- Luís M A Bettencourt
- T-7 and CNLS, Theoretical Division, MS B284 Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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281
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Synchrony and Asynchrony in a Fully Stochastic Neural Network. Bull Math Biol 2008; 70:1608-33. [DOI: 10.1007/s11538-008-9311-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2007] [Accepted: 02/12/2008] [Indexed: 10/22/2022]
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282
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Neuronal avalanches organize as nested theta- and beta/gamma-oscillations during development of cortical layer 2/3. Proc Natl Acad Sci U S A 2008; 105:7576-81. [PMID: 18499802 DOI: 10.1073/pnas.0800537105] [Citation(s) in RCA: 205] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Maturation of the cerebral cortex involves the spontaneous emergence of distinct patterns of neuronal synchronization, which regulate neuronal differentiation, synapse formation, and serve as a substrate for information processing. The intrinsic activity patterns that characterize the maturation of cortical layer 2/3 are poorly understood. By using microelectrode array recordings in vivo and in vitro, we show that this development is marked by the emergence of nested - and beta/gamma-oscillations that require NMDA- and GABA(A)-mediated synaptic transmission. The oscillations organized as neuronal avalanches, i.e., they were synchronized across cortical sites forming diverse and millisecond-precise spatiotemporal patterns that distributed in sizes according to a power law with a slope of -1.5. The correspondence between nested oscillations and neuronal avalanches required activation of the dopamine D(1) receptor. We suggest that the repetitive formation of neuronal avalanches provides an intrinsic template for the selective linking of external inputs to developing superficial layers.
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283
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Stegenga J, Le Feber J, Marani E, Rutten WLC. Analysis of cultured neuronal networks using intraburst firing characteristics. IEEE Trans Biomed Eng 2008; 55:1382-90. [PMID: 18390329 DOI: 10.1109/tbme.2007.913987] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
It is an open question whether neuronal networks, cultured on multielectrode arrays, retain any capability to usefully process information (learning and memory). A necessary prerequisite for learning is that stimulation can induce lasting changes in the network. To observe these changes, one needs a method to describe the network in sufficient detail, while stable in normal circumstances. We analyzed the spontaneous bursting activity that is encountered in dissociated cultures of rat neocortical cells. Burst profiles (BPs) were made by estimating the instantaneous array-wide firing frequency. The shape of the BPs was found to be stable on a time scale of hours. Spatiotemporal detail is provided by analyzing the instantaneous firing frequency per electrode. The resulting phase profiles (PPs) were estimated by aligning BPs to their peak spiking rate over a period of 15 min. The PPs reveal a stable spatiotemporal pattern of activity during bursts over a period of several hours, making them useful for plasticity and learning studies. We also show that PPs can be used to estimate conditional firing probabilities. Doing so, yields an approach in which network bursting behavior and functional connectivity can be studied.
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Affiliation(s)
- Jan Stegenga
- Institute of Biomedical Technology, Department of Electrical Engineering, University of Twente, Enschede, The Netherlands.
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284
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Raichman N, Ben-Jacob E. Identifying repeating motifs in the activation of synchronized bursts in cultured neuronal networks. J Neurosci Methods 2008; 170:96-110. [PMID: 18281097 DOI: 10.1016/j.jneumeth.2007.12.020] [Citation(s) in RCA: 60] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2007] [Revised: 12/23/2007] [Accepted: 12/30/2007] [Indexed: 11/15/2022]
Affiliation(s)
- Nadav Raichman
- School of Physics and Astronomy, Raymond & Beverly Sackler Faculty of Exact Sciences, Tel-Aviv University, Tel-Aviv, Israel.
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285
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Ribeiro TL, Copelli M. Deterministic excitable media under Poisson drive: power law responses, spiral waves, and dynamic range. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2008; 77:051911. [PMID: 18643106 DOI: 10.1103/physreve.77.051911] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2008] [Indexed: 05/26/2023]
Abstract
When each site of a spatially extended excitable medium is independently driven by a Poisson stimulus with rate h , the interplay between creation and annihilation of excitable waves leads to an average activity F . It has recently been suggested that in the low-stimulus regime (h approximately 0) the response function F(h) of hypercubic deterministic systems behaves as a power law, F approximately h{m} . Moreover, the response exponent m has been predicted to depend only on the dimensionality d of the lattice, m=1/(1+d) [T. Ohta and T. Yoshimura, Physica D 205, 189 (2005)]. In order to test this prediction, we study the response function of excitable lattices modeled by either coupled Morris-Lecar equations or Greenberg-Hastings cellular automata. We show that the prediction is verified in our model systems for d=1 , 2, and 3, provided that a minimum set of conditions is satisfied. Under these conditions, the dynamic range-which measures the range of stimulus intensities that can be coded by the network activity-increases with the dimensionality d of the network. The power law scenario breaks down, however, if the system can exhibit self-sustained activity (spiral waves). In this case, we recover a scenario that is common to probabilistic excitable media: as a function of the conductance coupling G among the excitable elements, the dynamic range is maximized precisely at the critical value G_{c} above which self-sustained activity becomes stable. We discuss the implications of these results in the context of neural coding.
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Affiliation(s)
- Tiago L Ribeiro
- Laboratório de Física Teórica e Computacional, Departamento de Física, Universidade Federal de Pernambuco, 50670-901 Recife, PE, Brazil.
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286
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Halley JD, Winkler DA. Critical-like self-organization and natural selection: Two facets of a single evolutionary process? Biosystems 2008; 92:148-58. [DOI: 10.1016/j.biosystems.2008.01.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2007] [Revised: 01/18/2008] [Accepted: 01/21/2008] [Indexed: 11/16/2022]
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287
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Nakamura T, Takumi T, Takano A, Aoyagi N, Yoshiuchi K, Struzik ZR, Yamamoto Y. Of mice and men--universality and breakdown of behavioral organization. PLoS One 2008; 3:e2050. [PMID: 18446212 PMCID: PMC2323110 DOI: 10.1371/journal.pone.0002050] [Citation(s) in RCA: 62] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2007] [Accepted: 03/13/2008] [Indexed: 12/02/2022] Open
Abstract
Mental or cognitive brain functions, and the effect on them of abnormal psychiatric diseases, are difficult to approach through molecular biological techniques due to the lack of appropriate assay systems with objective measures. We therefore study laws of behavioral organization, specifically how resting and active periods are interwoven throughout daily life, using objective criteria, and first discover that identical laws hold both for healthy humans subject to the full complexity of daily life, and wild-type mice subject to maximum environmental constraints. We find that active period durations with physical activity counts successively above a predefined threshold, when rescaled with individual means, follow a universal stretched exponential (gamma-type) cumulative distribution, while resting period durations below the threshold obey a universal power-law cumulative distribution with identical parameter values for both of the mammalian species. Further, by analyzing the behavioral organization of mice with a circadian clock gene (Period2) eliminated, and humans suffering from major depressive disorders, we find significantly lower parameter values (power-law scaling exponents) for the resting period durations in both these cases. Such a universality and breakdown of the behavioral organization of mice and humans, revealed through objective measures, is expected to facilitate the understanding of the molecular basis of the pathophysiology of neurobehavioral diseases, including depression, and lay the foundations for formulating a range of neuropsychiatric behavioral disorder models.
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Affiliation(s)
- Toru Nakamura
- The Center for Advanced Medical Engineering and Informatics, Osaka University, Osaka, Japan
| | | | | | - Naoko Aoyagi
- Educational Physiology Laboratory, Graduate School of Education, University of Tokyo, Tokyo, Japan
| | - Kazuhiro Yoshiuchi
- Department of Psychosomatic Medicine, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Zbigniew R. Struzik
- Educational Physiology Laboratory, Graduate School of Education, University of Tokyo, Tokyo, Japan
| | - Yoshiharu Yamamoto
- Educational Physiology Laboratory, Graduate School of Education, University of Tokyo, Tokyo, Japan
- * E-mail:
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288
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Werner G. Consciousness related neural events viewed as brain state space transitions. Cogn Neurodyn 2008; 3:83-95. [PMID: 19003465 DOI: 10.1007/s11571-008-9040-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2007] [Accepted: 03/25/2008] [Indexed: 10/22/2022] Open
Abstract
This theoretical and speculative essay addresses a categorical distinction between neural events of sensory-motor cognition and those presumably associated with consciousness. It proposes to view this distinction in the framework of the branch of Statistical Physics currently referred to as Modern Critical Theory (Stanley, Introduction to phase transitions and critical phenomena, 1987; Marro and Dickman, Nonequilibrium phase transitions in lattice, 1999). Based on established landmarks of brain dynamics, network configurations and their role for conveying oscillatory activity of certain frequencies bands, the question is examined: what kind of state space transitions can systems with these properties undergo, and could the relation between neural processes of sensory-motor cognition and those of events in consciousness be of the same category as is characterized by state transitions in non-equilibrium physical systems? Approaches for empirical validation of this view by suitably designed brain imaging studies, and for computational simulations of the proposed principle are discussed.
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Affiliation(s)
- Gerhard Werner
- Department of Biomedical Engineering, University of Texas, Austin, TX, USA,
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289
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Pasquale V, Massobrio P, Bologna LL, Chiappalone M, Martinoia S. Self-organization and neuronal avalanches in networks of dissociated cortical neurons. Neuroscience 2008; 153:1354-69. [PMID: 18448256 DOI: 10.1016/j.neuroscience.2008.03.050] [Citation(s) in RCA: 283] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2007] [Revised: 03/11/2008] [Accepted: 03/12/2008] [Indexed: 11/30/2022]
Abstract
Dissociated cortical neurons from rat embryos cultured onto micro-electrode arrays exhibit characteristic patterns of electrophysiological activity, ranging from isolated spikes in the first days of development to highly synchronized bursts after 3-4 weeks in vitro. In this work we analyzed these features by considering the approach proposed by the self-organized criticality theory: we found that networks of dissociated cortical neurons also generate spontaneous events of spreading activity, previously observed in cortical slices, in the form of neuronal avalanches. Choosing an appropriate time scale of observation to detect such neuronal avalanches, we studied the dynamics by considering the spontaneous activity during acute recordings in mature cultures and following the development of the network. We observed different behaviors, i.e. sub-critical, critical or super-critical distributions of avalanche sizes and durations, depending on both the age and the development of cultures. In order to clarify this variability, neuronal avalanches were correlated with other statistical parameters describing the global activity of the network. Criticality was found in correspondence to medium synchronization among bursts and high ratio between bursting and spiking activity. Then, the action of specific drugs affecting global bursting dynamics (i.e. acetylcholine and bicuculline) was investigated to confirm the correlation between criticality and regulated balance between synchronization and variability in the bursting activity. Finally, a computational model of neuronal network was developed in order to interpret the experimental results and understand which parameters (e.g. connectivity, excitability) influence the distribution of avalanches. In summary, cortical neurons preserve their capability to self-organize in an effective network even when dissociated and cultured in vitro. The distribution of avalanche features seems to be critical in those cultures displaying medium synchronization among bursts and poor random spiking activity, as confirmed by chemical manipulation experiments and modeling studies.
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Affiliation(s)
- V Pasquale
- Neuroscience and Brain Technology Department, Italian Institute of Technology, Via Morego 30, Genoa, Italy
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290
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Pratt KG, Dong W, Aizenman CD. Development and spike timing-dependent plasticity of recurrent excitation in the Xenopus optic tectum. Nat Neurosci 2008; 11:467-75. [PMID: 18344990 DOI: 10.1038/nn2076] [Citation(s) in RCA: 67] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2008] [Accepted: 02/20/2008] [Indexed: 11/10/2022]
Abstract
Much of the information processing in the brain occurs at the level of local circuits; however, the mechanisms underlying their initial development are poorly understood. We sought to examine the early development and plasticity of local excitatory circuits in the optic tectum of Xenopus laevis tadpoles. We found that retinal input recruits persistent, recurrent intratectal synaptic excitation that becomes more temporally compact and less variable over development, thus increasing the temporal coherence and precision of tectal cell spiking. We also saw that patterned retinal input can sculpt recurrent activity according to a spike timing-dependent plasticity rule, and that impairing this plasticity during development results in abnormal refinement of the temporal characteristics of recurrent circuits. This plasticity is a previously unknown mechanism by which patterned retinal activity allows intratectal circuitry to self-organize, optimizing the temporal response properties of the tectal network, and provides a substrate for rapid modulation of tectal neuron receptive-field properties.
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Affiliation(s)
- Kara G Pratt
- Department of Neuroscience, Brown University, Box G-LN, Providence, Rhode Island 02912, USA
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291
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Madhavan R, Chao ZC, Wagenaar DA, Bakkum DJ, Potter SM. Multi-site stimulation quiets network-wide spontaneous bursts and enhances functional plasticity in cultured cortical networks. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2008; 2006:1593-6. [PMID: 17946052 DOI: 10.1109/iembs.2006.260571] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
We culture high-density cortical cultures on multi-electrode arrays (MEAs), which allow us to stimulate and record from thousands of neurons. One of the modes of activity in these high-density cultures is dish-wide synchronized bursting. Unlike in vivo, these synchronized patterns persist for the lifetime of the culture. Such aberrant patterns of activity might be due to the fact that cortical cultures are sensory-deprived and arrested in development. We have devised methods to control this spontaneous activity by multi-electrode electrical stimulation and to study long-term functional neural plasticity, on a background of such burst-quieting stimulation. Here, we investigate whether burst quieting reveals long-term plasticity induced by tetanic stimulation. Spatio-temporal activity patterns (STAPs) that result from probe pulses were clustered and quantified in quieted and non-quieted cultures. Burst-quieted cultures show more tetanus-induced functional change than cultures which are allowed to express spontaneous bursts. The methods developed for this study will help in the understanding of network dynamics and appreciation of their role in long-term plasticity and information processing in the brain.
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292
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Kang S, Kitano K, Fukai T. Structure of spontaneous UP and DOWN transitions self-organizing in a cortical network model. PLoS Comput Biol 2008; 4:e1000022. [PMID: 18369421 PMCID: PMC2265465 DOI: 10.1371/journal.pcbi.1000022] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2007] [Accepted: 02/05/2008] [Indexed: 12/02/2022] Open
Abstract
Synaptic plasticity is considered to play a crucial role in the experience-dependent self-organization of local cortical networks. In the absence of sensory stimuli, cerebral cortex exhibits spontaneous membrane potential transitions between an UP and a DOWN state. To reveal how cortical networks develop spontaneous activity, or conversely, how spontaneous activity structures cortical networks, we analyze the self-organization of a recurrent network model of excitatory and inhibitory neurons, which is realistic enough to replicate UP-DOWN states, with spike-timing-dependent plasticity (STDP). The individual neurons in the self-organized network exhibit a variety of temporal patterns in the two-state transitions. In addition, the model develops a feed-forward network-like structure that produces a diverse repertoire of precise sequences of the UP state. Our model shows that the self-organized activity well resembles the spontaneous activity of cortical networks if STDP is accompanied by the pruning of weak synapses. These results suggest that the two-state membrane potential transitions play an active role in structuring local cortical circuits.
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Affiliation(s)
- Siu Kang
- Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute, Wako, Japan
| | - Katsunori Kitano
- Department of Computer Science, Ritsumeikan University, Shiga, Japan
| | - Tomoki Fukai
- Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute, Wako, Japan
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293
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Beggs JM. The criticality hypothesis: how local cortical networks might optimize information processing. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2008; 366:329-43. [PMID: 17673410 DOI: 10.1098/rsta.2007.2092] [Citation(s) in RCA: 218] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Early theoretical and simulation work independently undertaken by Packard, Langton and Kauffman suggested that adaptability and computational power would be optimized in systems at the 'edge of chaos', at a critical point in a phase transition between total randomness and boring order. This provocative hypothesis has received much attention, but biological experiments supporting it have been relatively few. Here, we review recent experiments on networks of cortical neurons, showing that they appear to be operating near the critical point. Simulation studies capture the main features of these data and suggest that criticality may allow cortical networks to optimize information processing. These simulations lead to predictions that could be tested in the near future, possibly providing further experimental evidence for the criticality hypothesis.
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Affiliation(s)
- John M Beggs
- Department of Physics, Indiana University, Bloomington, IN 47405, USA.
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294
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Tang A, Jackson D, Hobbs J, Chen W, Smith JL, Patel H, Prieto A, Petrusca D, Grivich MI, Sher A, Hottowy P, Dabrowski W, Litke AM, Beggs JM. A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro. J Neurosci 2008; 28:505-18. [PMID: 18184793 PMCID: PMC6670549 DOI: 10.1523/jneurosci.3359-07.2008] [Citation(s) in RCA: 188] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2007] [Revised: 11/29/2007] [Accepted: 12/03/2007] [Indexed: 11/21/2022] Open
Abstract
Multineuron firing patterns are often observed, yet are predicted to be rare by models that assume independent firing. To explain these correlated network states, two groups recently applied a second-order maximum entropy model that used only observed firing rates and pairwise interactions as parameters (Schneidman et al., 2006; Shlens et al., 2006). Interestingly, with these minimal assumptions they predicted 90-99% of network correlations. If generally applicable, this approach could vastly simplify analyses of complex networks. However, this initial work was done largely on retinal tissue, and its applicability to cortical circuits is mostly unknown. This work also did not address the temporal evolution of correlated states. To investigate these issues, we applied the model to multielectrode data containing spontaneous spikes or local field potentials from cortical slices and cultures. The model worked slightly less well in cortex than in retina, accounting for 88 +/- 7% (mean +/- SD) of network correlations. In addition, in 8 of 13 preparations, the observed sequences of correlated states were significantly longer than predicted by concatenating states from the model. This suggested that temporal dependencies are a common feature of cortical network activity, and should be considered in future models. We found a significant relationship between strong pairwise temporal correlations and observed sequence length, suggesting that pairwise temporal correlations may allow the model to be extended into the temporal domain. We conclude that although a second-order maximum entropy model successfully predicts correlated states in cortical networks, it should be extended to account for temporal correlations observed between states.
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Affiliation(s)
| | | | | | | | - Jodi L. Smith
- School of Medicine, Indiana University, Bloomington, Indiana 47405
| | - Hema Patel
- School of Medicine, Indiana University, Bloomington, Indiana 47405
| | | | - Dumitru Petrusca
- Institute for Particle Physics, University of California, Santa Cruz, California 95064, and
| | - Matthew I. Grivich
- Institute for Particle Physics, University of California, Santa Cruz, California 95064, and
| | - Alexander Sher
- Institute for Particle Physics, University of California, Santa Cruz, California 95064, and
| | - Pawel Hottowy
- Institute for Particle Physics, University of California, Santa Cruz, California 95064, and
| | - Wladyslaw Dabrowski
- Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, 30-059, Krakow, Poland
| | - Alan M. Litke
- Institute for Particle Physics, University of California, Santa Cruz, California 95064, and
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295
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Abstract
After initially presenting evidence that the electrical activity recorded from the brain surface can reflect metastable state transitions of neuronal configurations at the mesoscopic level, I will suggest that their patterns may correspond to the distinctive spatio-temporal activity in the dynamic core (DC) and the global neuronal workspace (GNW), respectively, in the models of the Edelman group on the one hand, and of Dehaene-Changeux, on the other. In both cases, the recursively reentrant activity flow in intra-cortical and cortical-subcortical neuron loops plays an essential and distinct role. Reasons will be given for viewing the temporal characteristics of this activity flow as signature of self-organized criticality (SOC), notably in reference to the dynamics of neuronal avalanches. This point of view enables the use of statistical physics approaches for exploring phase transitions, scaling and universality properties of DC and GNW, with relevance to the macroscopic electrical activity in EEG and EMG.
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Affiliation(s)
- Gerhard Werner
- Department of Biomedical Engineering, University of Texas, Austin, Engineering Science Building, 1 University Station, C0800 Austin, TX 78712-0238, USA.
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296
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Wheeler BC. Building a brain on a chip. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2008; 2008:1604-1606. [PMID: 19162982 DOI: 10.1109/iembs.2008.4649479] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
The wild idea that nerve cells grown in culture could have reliable computational function, while still a wild idea, is closer to reality than is reasonable to expect, thanks to applications of both engineering and applied biology. The metaphor works both ways: applications of more traditional engineering technologies - signal processing, electronics, microlithography, materials science - make possible the controlled growth, recording, and stimulation of nerve cells. In turn the goal is to design, construct, test, and utilize - in short to engineer - a working biological construct. In this lecture examples, mainly from the speaker's laboratory, illustrate the component technologies that have been utilized in this pursuit, as well as examples illustrating how the approaching the problem as an engineer leads to the asking new questions. The talk will include brief discussion of the problem of analyzing high dimensional, inherently non-stationary neural spike data.
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Affiliation(s)
- Bruce C Wheeler
- University of Florida, Crayton Pruitt Family Department of Biomedical Engineering, Gainesville, USA.
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297
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Assis VRV, Copelli M. Dynamic range of hypercubic stochastic excitable media. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2008; 77:011923. [PMID: 18351892 DOI: 10.1103/physreve.77.011923] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2007] [Revised: 12/11/2007] [Indexed: 05/26/2023]
Abstract
We study the response properties of d-dimensional hypercubic excitable networks to a stochastic stimulus. Each site, modeled either by a three-state stochastic susceptible-infected-recovered-susceptible system or by the probabilistic Greenberg-Hastings cellular automaton, is continuously and independently stimulated by an external Poisson rate h. The response function (mean density of active sites rho versus h) is obtained via simulations (for d=1,2,3,4) and mean-field approximations at the single-site and pair levels (for all d). In any dimension, the dynamic range and sensitivity of the response function are maximized precisely at the nonequilibrium phase transition to self-sustained activity, in agreement with a reasoning recently proposed. Moreover, the maximum dynamic range attained at a given dimension d is a decreasing function of d.
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Affiliation(s)
- Vladimir R V Assis
- Laboratório de Física Teórica e Computacional, Departamento de Física, Universidade Federal de Pernambuco, 50670-901 Recife, PE, Brazil.
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298
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Abstract
We present a simple Markov model of spiking neural dynamics that can be analytically solved to characterize the stochastic dynamics of a finite-size spiking neural network. We give closed-form estimates for the equilibrium distribution, mean rate, variance, and autocorrelation function of the network activity. The model is applicable to any network where the probability of firing of a neuron in the network depends on only the number of neurons that fired in a previous temporal epoch. Networks with statistically homogeneous connectivity and membrane and synaptic time constants that are not excessively long could satisfy these conditions. Our model completely accounts for the size of the network and correlations in the firing activity. It also allows us to examine how the network dynamics can deviate from mean field theory. We show that the model and solutions are applicable to spiking neural networks in biophysically plausible parameter regimes.
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Affiliation(s)
- Hédi Soula
- Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
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299
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Spontaneous coordinated activity in cultured networks: Analysis of multiple ignition sites, primary circuits, and burst phase delay distributions. J Comput Neurosci 2007; 24:346-57. [DOI: 10.1007/s10827-007-0059-1] [Citation(s) in RCA: 47] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2007] [Revised: 10/03/2007] [Accepted: 10/17/2007] [Indexed: 10/22/2022]
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300
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Alcaro A, Huber R, Panksepp J. Behavioral functions of the mesolimbic dopaminergic system: an affective neuroethological perspective. BRAIN RESEARCH REVIEWS 2007; 56:283-321. [PMID: 17905440 PMCID: PMC2238694 DOI: 10.1016/j.brainresrev.2007.07.014] [Citation(s) in RCA: 295] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2006] [Revised: 07/03/2007] [Accepted: 07/03/2007] [Indexed: 12/11/2022]
Abstract
The mesolimbic dopaminergic (ML-DA) system has been recognized for its central role in motivated behaviors, various types of reward, and, more recently, in cognitive processes. Functional theories have emphasized DA's involvement in the orchestration of goal-directed behaviors and in the promotion and reinforcement of learning. The affective neuroethological perspective presented here views the ML-DA system in terms of its ability to activate an instinctual emotional appetitive state (SEEKING) evolved to induce organisms to search for all varieties of life-supporting stimuli and to avoid harms. A description of the anatomical framework in which the ML system is embedded is followed by the argument that the SEEKING disposition emerges through functional integration of ventral basal ganglia (BG) into thalamocortical activities. Filtering cortical and limbic input that spreads into BG, DA transmission promotes the "release" of neural activity patterns that induce active SEEKING behaviors when expressed at the motor level. Reverberation of these patterns constitutes a neurodynamic process for the inclusion of cognitive and perceptual representations within the extended networks of the SEEKING urge. In this way, the SEEKING disposition influences attention, incentive salience, associative learning, and anticipatory predictions. In our view, the rewarding properties of drugs of abuse are, in part, caused by the activation of the SEEKING disposition, ranging from appetitive drive to persistent craving depending on the intensity of the affect. The implications of such a view for understanding addiction are considered, with particular emphasis on factors predisposing individuals to develop compulsive drug seeking behaviors.
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Affiliation(s)
- Antonio Alcaro
- Department of Biological Sciences and J.P. Scott Center for Neuroscience, Mind & Behavior, Bowling Green State University, Life Science Building, Bowling Green, OH, 43403, USA
- Santa Lucia Foundation, European Centre for Brain Research (CERC), Via del Fosso di Fiorano 65, 00143 Rome, Italy
| | - Robert Huber
- Department of Biological Sciences and J.P. Scott Center for Neuroscience, Mind & Behavior, Bowling Green State University, Life Science Building, Bowling Green, OH, 43403, USA
| | - Jaak Panksepp
- Department of Biological Sciences and J.P. Scott Center for Neuroscience, Mind & Behavior, Bowling Green State University, Life Science Building, Bowling Green, OH, 43403, USA
- Department of VCAPP, Center for the Study of Animal Well-Being, College of Veterinary Medicine, Washington State University, Pullman, WA 99163, USA
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